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MyoChallenge: Learning contact-rich manipulation using a musculoskeletal hand

Vittorio Caggiano · · Guillaume Durandau · Seungmoon Song · Yuval Tassa · Massimo Sartori · Vikash Kumar



Manual dexterity has been considered one of the critical components for human evolution. The ability to perform movements as simple as holding and rotating an object in the hand without dropping it needs the coordination of more than 35 muscles which, act synergistically or antagonistically on multiple joints. They control the flexion and extension of the joints connecting the bones which in turn allow the manipulation to happen. This complexity in control is markedly different than typical pre-specified movements or torque based controls used in robotics. In this competition - MyoChallenge, participant will develop controllers for a realistic hand to solve a series of dexterous manipulation tasks. Participant will be provided with a physiologically accurate and efficient neuromusculoskeletal human hand model developed in the (free) MuJoCo physics simulator. In addition the provided model has also contact rich capabilities. Participant will be interfacing with a standardized training environment to help build the controllers. The final score will then be based on a environment with unknown parameters.This challenge builds on 3 previous NeurIPS challenge on controlling legs mus- culoskeletal model for locomotion, which attracted about 1300 participants and generated 8000 submissions, which produced 9 academic publications. This chal- lenge will leverage the experience and knowledge from the previous challenges and will further establish neuromusculoskeletal modelling as a benchmarks for the neuromuscular control and machine learning community.In addition of providing challenges for the biomechanics and machine learning community, this challenge will provide new opportunities to explore solutions that will inspire the robotic, medical and rehabilitation fields on one of the most complex dexterous skills humans are able to perform.